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Now showing items 11-18 of 18
A capacitated mobile facility location problem with mobile demand: Recurrent service provision to en route refugees
(OpenProceedings.org, 2022)
In this paper, we help humanitarian organizations provide service via mobile facilities (MFs) to migrating refugees, who attempt to cross international borders. Over a planning horizon, we aim to optimize number and routes ...
Stochastic production planning with flexible manufacturing systems and uncertain demand: A column generation-based approach
(Elsevier, 2022)
The ongoing pandemic, namely COVID-19, has rendered widespread economic disorder. The deficiencies have delayed production at manufacturers in several industries on the supply side. The effects of disruption were more ...
Deep reinforcement learning approach for trading automation in the stock market
(IEEE, 2022)
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price 'prediction' ...
A mathematical model for equitable in-country COVID-19 vaccine allocation
(Taylor and Francis, 2022)
Given the scarcity of COVID-19 vaccines, equitable (fair) allocation of limited vaccines across the main administrative units of a country (e.g. municipalities) has been an important concern for public health authorities ...
A machine learning approach to deal with ambiguity in the humanitarian decision-making
(Wiley, 2023-09)
One of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in ...
A predictive multistage postdisaster damage assessment framework for drone routing
(Wiley, 2024-01)
This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an ...
Robust reformulations of ambiguous chance constraints with discrete probability distributions
(Balikesir University, 2019)
This paper proposes robust reformulations of ambiguous chance constraints when the underlying family of distributions is discrete and supported in a so-called ``p-box'' or ``p-ellipsoidal'' uncertainty set. Using the robust ...
Effective training methods for automatic musical genre classification
(SciTePress, 2019)
Musical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by ...
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